Investigating Groups#

In a scenario where you have pre-existing labels for your documents, like let’s say genre, or a domain from which you scraped them, topicwizard allows you to investigate these groups’ relation to topics and words.

Web App#

If you have pre-labelled documents you can pass these document labels to visualize(), this will implicitly add a “Groups” page to the web application. In this example I am going to use the 20 newsgroups dataset from scikit-learn.

import topicwizard
from sklearn.datasets import fetch_20newsgroups

newsgroups = fetch_20newsgroups(subset="all")
corpus = newsgroups.data
# Sklearn gives the labels back as integers, we have to map them back to
# the actual textual label.
group_labels = np.array(newsgroups.target_names)[newsgroups.target]

# Here we fit a topic model to the corpus
pipeline = make_pipeline(
    CountVectorizer(stop_words="english", max_df=0.5, min_df=10),
    NMF(n_components=30),
).fit(corpus)

# Notice that I'm passing the labels as the group_labels argument
topicwizard.visualize(corpus=corpus, pipeline=pipeline, group_labels=group_labels)

This page will be added to the web application:

Screenshot of groups.

To get more information about how to interpret the graphs and how they are produced, hover your cursor over the helper in the corner.

Self-Contained Plots#

It might be an overkill for you to display the entire page, and you might want static html plots instead of the entire application running. This can be particulary useful for reports with DataPane or Jupyter Notebooks.

Group Map#

You can display the group map as a standalone plot, with the groups being colored according to dominant topic. Just like on the document map you can choose whether you would like to use the term or the topic representations for displaying the map.

from topicwizard.figures import group_map

group_map(corpus, group_labels, pipeline=pipeline, representation="term")

group_map(corpus, group_labels, pipeline=pipeline, representation="topic")

Group Topic Barcharts#

You can create a joint plot of the topic content of all groups. These will be displayed as bar charts.

from topicwizard.figures import group_topic_barcharts

group_topic_barcharts(corpus, group_labels, pipeline=pipeline, top_n=5)

Word Clouds#

You can create word clouds for each of the group labels. This will only take word counts into account and not relevance.

from topicwizard.figures import group_wordclouds

group_wordclouds(corpus, group_labels, pipeline=pipeline)